Image document processing device, image document processing method, program, and storage medium

ABSTRACT

An image document processing device extracts a character sequence image having M number of characters in an image document, divides the image into individual character images, extracts features of the individual character images, and based on the features, selects N (N is an integer more than 1) character images in the order of degree of matching from a font-feature dictionary for storing features of all character images according to fonts, and generates an M×N index matrix for the extracted character sequence. In searching, the device searches an index-information storage section with respect to each search character included in a search keyword in an input search expression, and extracts an image document including an index matrix including the search keyword. This provides an image document processing device and an image document processing method each allowing indexing not requiring user&#39;s operation and each allowing highly precise searching without OCR recognition.

This non-provisional application claims priority under 35 U.S.C. §119(a) on Patent Application No. 200710000961.2 filed in The People'sRepublic of China on Jan. 15, 2007, the entire contents of which arehereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to an image document processing device andan image document processing method, each of which allows reception of adocument as an image and storage of the image. In particular, thepresent invention relates to an image document processing device and animage document processing method having a function of searching imagedocuments that have been stored.

BACKGROUND OF THE INVENTION

A document-filing device electronically accumulates documents byconverting the documents into images by use of an image input devicesuch as an image scanner, allowing a user to search for a documentlater. The document-filing device has been put into practical use.

In order to search an image document having been read as image data, itis necessary to manually assign index information for searching to eachimage document. This is very troublesome.

Further, there is proposed a device that specifies the position of acharacter region (text region), performs OCR (Optical Character Reader)recognition, and thus allows full text search according to contents ofthe text. An example of a conventional technique that uses OCRrecognition is disclosed in Japanese Unexamined Patent Publication No.1995-152774 (Tokukaihei 7-152774).

However, OCR recognition is problematic in that it requires much amountof calculation, which takes much time. Further, OCR recognition does notattain a high ratio in recognition of characters, which may result inthat characters are wrongly recognized and are not searched.Consequently, OCR recognition is problematic in terms of accuracy insearch.

On the other hand, Japanese Unexamined Patent Publication No. 1998-74250(Tokukaihei 10-74250) discloses a technique for allowing automatic fulltext search without using OCR recognition.

In the technique disclosed in Japanese Unexamined Patent Publication No.1998-74250, there is prepared a category dictionary in which charactersare classified in advance into similar-character categories with respectto every similar-characters according to image features. In registeringan image document, each character of a text region (character region) isnot recognized as a character, but an image feature of the character isextracted, and the character is classified into a character categoryaccording to the image feature, and a category sequence recognized foreach character is stored in combination with an input image. Insearching the image document, each character of a search keyword isconverted into a corresponding category, and an image document partiallyincluding a category sequence derived from the conversion is extractedas a result of the search.

It is described that the technique provides a document filing thatallows high-speed registration of a document with small calculationpower and allows a search with little omissions.

However, Japanese Unexamined Patent Publication No. 1998-74250 has thefollowing problems.

In the technique disclosed in this publication, with respect to eachsimilar-character category, a representative vector that is an averageof feature vectors of characters belonging to the similar-charactercategory is determined, and any one of character codes of the charactersbelonging to the similar-character category is determined as arepresentative character code.

When an image document is registered, a feature vector of each characterimage included in a text region of the image document is matched withthe representative vector of the similar-character category, and asimilar-character category to which each character belongs isidentified. The character image included in the text region is replacedwith a representative character code of the identified similar-charactercategory, and character images are stored as a representative charactercode sequences.

However, although the technique for matching a feature vector of arecognized character with a representative vector has less amount ofcalculation, the technique results in less exact result of matching thana technique for directly matching a feature vector of a recognizedcharacter with a feature vector of each character. This may result inomission in searching. Further, such matching and subsequent indexingare generally performed while off-lined, and therefore are not soconvenient for a user. More exact matching is preferable for the user.

Further, the technique disclosed in this publication has a problem alsoin searching. When searching is performed, a search keyword is convertedinto a sequence of representative character codes for categories thatinclude characters of the search keyword, with reference to a charactercode/category correspondence table. Then, the sequence of representativecharacter codes having been converted from the keyword is searched forfrom sequences of representative character codes that are obtained fromregistered image documents, specifically, by use of index made of thesequences of representative character codes.

However, searching by converting the keyword into the sequence ofrepresentative character codes does not allow specifying the position ofa character of the keyword in a similar-character category.Consequently, characters belonging to the same similar-charactercategory show the same degree of relevance regardless of whether thecharacters are more similar or less similar. As a result, it isimpossible to present image documents exactly in the order sequentiallyfrom the most relevant image document.

SUMMARY OF THE INVENTION

An object of the present invention is to provide an image documentprocessing device and an image document processing method each of whichrealizes an indexing function not requiring user's operation and notrequiring OCR recognition, and each of which allows highly accuratesearching.

In order to solve the foregoing problem, the image document processingdevice of the present invention includes: a font-feature dictionary forstoring features of images of individual characters; acharacter-sequence clipping section for clipping an image of a charactersequence including a plurality of characters in an input image document;an image-feature extracting section for dividing the image of thecharacter sequence into images of individual characters and extractingfeatures of the images of individual characters; a feature matchingsection for selecting N (N is an integer of more than 1) images ofcharacters as candidate characters in an order of high degree ofmatching of features so as to generate an M×N index matrix where M (M isan integer of more than 1) is the number of characters in the charactersequence, the candidate characters being selected from the font-featuredictionary based on the features of the images of individual charactersthat have been extracted by the image-feature extracting section; anindex-information storage section for storing the index matrix so thatthe index matrix is related to the input image document; and a searchsection for searching the index-information storage section with respectto each search character included in a search keyword in an input searchexpression, and extracting one or more image documents each including anindex matrix that includes the search character.

With the arrangement, the image-feature extracting section divides, intoimages of individual characters, the image of the character sequence inthe image document that is specified and clipped by thecharacter-sequence clipping section, and the image-feature extractingsection extracts features of the images of individual characters. Then,the feature matching section selects N (N is an integer of more than 1)images of characters as candidate characters in an order of high degreeof matching of features so as to generate an M×N index matrix for theextracted character sequence, the candidate characters being selected,based on the features of the images of individual characters, from thefont-feature dictionary for storing features of images of individualcharacters.

The index-information storage section stores the generated index matrixso that the index-matrix is related to the input image document. Insearching, the index matrix is used as index information for searchingan image document.

Consequently, it is possible to automatically specify a charactersequence in a character region of an image document without user'soperation and without OCR recognition, and to generate index informationfor the image document based on a feature of an image of the specifiedcharacter sequence.

Further, because features of images of individual characters areextracted and a plurality of candidate characters whose image featuresare similar are selected, suitably setting the number of the selectedcandidate characters allows exact searching without spending much timein recognition of characters unlike OCR recognition, and withoutomission of recognition.

Further, the font-feature dictionary is generated to store features ofimages of individual characters, and the feature matching sectiondivides the image of the extracted character sequence into images ofindividual characters to perform matching of features of the images.This allows the generated index matrix to have high accuracy.

In searching, the search section searches the index-information storagesection with respect to each search character included in a searchkeyword in an input search expression, and extracts one or more imagedocuments each including an index matrix that includes the searchcharacter.

In this manner, index matrices are analyzed with respect to each searchcharacter included in a search keyword so as to detect an index matrixincluding the search keyword. This assures total searching based onsearching for single characters.

This allows providing an image document processing device which realizesan indexing function not requiring user's operation and not requiringOCR recognition, and which allows highly accurate searching.

Further, the image document processing method of the present inventionincludes the steps of: (I) clipping an image of a character sequenceincluding a plurality of characters in an input image document; (II)dividing the image of the character sequence into images of individualcharacters and extracting features of the images of individualcharacters; (III) selecting N (N is an integer of more than 1) images ofcharacters as candidate characters in an order of high degree ofmatching of features so as to generate an M×N index matrix where M (M isan integer of more than 1) is the number of characters in the charactersequence, the candidate characters being selected, based on the featuresof the images of individual characters that have been extracted in thestep (II), from a font-feature dictionary for storing features of imagesof individual characters; (IV) storing the index matrix so that theindex matrix is related to the input image document; and (V) searchingthe index-information with respect to each search character included ina search keyword in an input search expression, and extracting one ormore image documents each including an index matrix that includes thesearch character.

This allows providing an image document processing method which realizesan indexing function not requiring user's operation and not requiringOCR, and which allows highly accurate searching, as already stated inthe explanation as to the image document processing device of thepresent invention.

For a fuller understanding of the nature and advantages of theinvention, reference should be made to the ensuing detailed descriptiontaken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of an embodiment of the presentinvention, illustrating a function of an image document processingdevice.

FIG. 2 is a block diagram of the image document processing device.

FIG. 3 is an explanatory drawing for illustrating a process in which theimage document processing device generates a font-sample database.

FIG. 4 is an explanatory drawing for illustrating a feature of aperiphery of a character image used in a character-image-featureextracting section in the image document processing device.

FIGS. 5( a) and 5(b) are explanatory drawings each for illustrating afeature of a grid direction used in the character-image-featureextracting section of the image document processing device.

FIG. 6 is an explanatory drawing for illustrating a process in which theimage document processing device generates a font-feature dictionary.

FIG. 7 is an explanatory drawing for illustrating a process in which theimage document processing device generates an index-informationdatabase.

FIG. 8 is an explanatory drawing for illustrating a specific example ofa process in which the image document processing device generates anindex matrix.

FIG. 9 is an explanatory drawing for illustrating: an example of animage document processed by the image document processing device; and anexample of data disposition of index information of the image documentin an index-information database.

FIG. 10 is an explanatory drawing for illustrating a function of asearch section and a search process in the image document processingdevice.

FIG. 11 is a flowchart illustrating a search procedure of the searchsection.

FIG. 12 is an explanatory drawing for illustrating a method in which thesearch section calculates similarity-degree between a search keyword andan index matrix.

FIG. 13 is an explanatory drawing for illustrating a specific example ofcalculation of the similarity-degree between a search keyword and anindex matrix.

FIG. 14 is an explanatory drawing for illustrating a search process witha semantic-analysis function in the image document processing device.

DESCRIPTION OF THE EMBODIMENTS

The present invention relates to indexing and searching of imagedocuments on the basis of recognition of features of images and matchingof the images. As a more preferable embodiment, the following disclosesa method and a device each of which allows generation of indexinformation of image documents based on significant character regions ofthe image documents and allows searching the generated indexinformation.

The following explains one embodiment of the present invention withreference to FIGS. 1 to 14. The present invention is not limited to thisembodiment.

FIG. 2 is a block diagram of an image document processing device of thepresent embodiment. In FIG. 2, a reference numeral 1 indicates akeyboard used for input of a search keyword and change of set valuessuch as the number of candidate characters, a similarity value, and asimilarity-degree weighting factor Q for rows. These set values will beexplained later.

A reference numeral 2 indicates an image scanner for acquiring an imagedocument. Note that, acquisition of an image document is performed notonly by the image scanner 2, but also by communication via a networketc. Further, the image scanner 2 can receive an input of a searchkeyword.

A reference numeral 3 indicates a display device for outputting anddisplaying a searched image document. Examples of what is displayedinclude information of similarity-degree and information of the name ofan image.

A reference numeral 4 indicates a processor for performing (i) animage-document-feature extracting process for extracting from an imagedocument a significant region (including a headline region) that servesas a key for searching, (ii) an index-information generating process forgenerating index information that allows searching of an image document,and (iii) a searching process based on the generated index information.A reference numeral 5 is an external storage device for storing softwareetc. with which the processor 4 performs the above processes.

The above processes are performed by the processor 4 executing thesoftware stored in the external storage device 5. An example of theprocessor 4 is a main body of a general computer. In the presentembodiment, the processor 4 also performs a font-feature dictionarygenerating process for generating a later-mentioned font-featuredictionary 15 (see FIG. 1) that is used in theindex-information-generating process.

The external storage device 5 may be a hard disc etc. that allows fastaccess. The external storage device 5 may be a high-capacity device suchas an optical disc in order to store a large amount of image documents.

The external storage device 5 constitutes the font-feature dictionary15, an index-information database 17, an image-document database 19, afont-sample database 13 etc. that will be mentioned later.

FIG. 1 is a functional block diagram of the embodiment of the presentinvention, illustrating a function of an image document processingdevice.

As illustrated in FIG. 1, the image document processing device of thepresent embodiment includes a character-database input section(character-DB input section) 11, a character-style normalizing section12, the font-sample database (font-sample DB) 13, acharacter-image-feature extracting section (image-feature-extractingsection) 14, the font-feature dictionary 15, a feature-matching section(feature-matching section) 16, the index-information database(index-information DB: index information storage section) 17, asignificant-region initial processing section (character-sequenceclipping section) 18, the image-document database (image document DB)19, an image-document-feature database (image-document-feature DB) 20,an image-document input section 21, a search section 22, asemantic-analysis section 23, a keyword input section 24, and asearch-result display section 25.

Among them, the character-DB input section 11, the character-stylenormalizing section 12, the font-sample DB 13, thecharacter-image-feature extracting section 14, and the font-featuredictionary 15 constitute a font-feature dictionary generating section 30that performs the aforementioned font-feature dictionary generatingprocess.

The following explains the functional blocks 11, 12, 13, 14, and 15 thatconstitute the font-feature dictionary generating section 30.

The character-DB input section 11 is a section to which is input a basiccharacter database necessary for generating the font-feature dictionary15. In a case where the device of the present invention can deal withChinese language, all of 6763 characters according to GB2312 in thePeople's Republic of China and the like are input to the character-DBinput section 11. In a case where the device of the present inventioncan deal with Japanese language, approximately 3000 characters accordingto JIS level-1 and the like are input to the character-DB input section11. That is, “characters” used here include codes. The character-DBinput section 11 includes the processor 4, and a character database issupplied via a storage medium or a network etc.

The character-style normalizing section 12 generates character imageshaving different fonts for all characters included in the characterdatabase having been input to the character-DB input section 11. Thecharacter images having different fonts are stored in the font-sample DB13.

FIG. 3 illustrates a process in which the character-style normalizingsection 12 generates the font sample DB 13. In the case where the deviceof the present invention can deal with Chinese language, thecharacter-style normalizing section 12 includes a font sample 12 a suchas Song typeface

imitated Song typeface

Hei typeface

and regular script

In the case where the device of the present invention can deal withJapanese language, the character-style normalizing section 12 includes afont sample such as MS Min typeface and MS gothic typeface.

A deforming section 12 b of the character-style normalizing section 12makes characters of the character database be images, and standardizethe character images. Then, referring to the font sample 12 a, thedeforming section 12 b deforms the character images having beenstandardized and thus makes them be character images having furtherdifferent fonts. Examples of deforming include clouding,enlarging/contracting, and miniaturizing. Character images having beensubjected to such deforming are caused by a character-style standardsection 12 c to be stored in the font sample DB13 as standard characterimages.

The font sample DB 13 stores standard character images for each of allcharacters included in the character database. Each character isprovided with plural standard character images that are different withrespect to each font determined by its character style and charactersize. For example, a character

is provided with plural standard character images

that are different in their shapes according to predetermined characterstyles, and that are different in their sizes according to predeterminedsizes.

The character-image-feature extracting section 14 extracts the featureof a character image (image feature) and stores the feature in thefont-feature dictionary 15. In the present embodiment, thecharacter-image-feature extracting section 14 extracts the features of acharacter image on the basis of a combination of (i) the feature of aperiphery of a character image and (ii) the feature of a grid direction,and the character-image-feature extracting section 14 regards thefeatures of a character image as feature vectors. The features of acharacter image are not limited to these. Other features may beextracted and regarded as feature vectors.

The following explains (i) the feature of a periphery of a characterimage and (ii) the feature of a grid direction. FIG. 4 is an explanatorydrawing for illustrating the feature of a periphery of a characterimage. The feature of a periphery of a character image is a feature of aperiphery seen from the outside of the character image. As illustratedin FIG. 4, a character image is scanned from four sides of a rectanglesurrounding the character image, and a distance from the side to a pointwhere a white pixel changes to a black pixel is regarded as a feature,and a position where a first change from a white pixel to a black pixeloccurs and a position where a second change from a white pixel to ablack pixel occurs are extracted.

For example, in a case where a rectangle surrounding a character imageis divided into X rows and Y columns, the character image is scannedfrom a left direction and from a right direction with respect to eachrow, and the character image is scanned from an upper direction and froma lower direction with respect to each column. FIG. 4 illustratesscanning of the character image from the left with respect to each row.

Further, in FIG. 4, a full-line arrow 1 indicates a scanning track froma side of the surrounding rectangle to a point where a first change froma white pixel to a black pixel occurs. A broken-line arrow 2 indicates ascanning track from a side of the surrounding rectangle to a point wherea second change from a white pixel to a black pixel occurs. A full-linearrow 3 indicates a scanning track on which a point where a change froma white pixel to a black pixel occurs is not detected. In the case ofthe full-line arrow 3, a distance value is 0.

On the other hand, FIGS. 5( a) and 5(b) are explanatory drawings forillustrating the feature of a grid direction. A character image isdivided into a rough grid and black pixels in each grid unit aresearched for in predetermined directions. The number of black pixelsthat are connected in individual directions is counted, and directioncontributivity indicative of how the black pixels are distributed inindividual directions is calculated by dividing a distance value by avalue corresponding to a difference in the number of the black pixels,with a Euclidean distance being a discrimination function.

In FIG. 5( a), the character image is divided into a grid having 16(4×4) grid units, and black pixels are searched for in three directions,i.e. an X-axis direction (0°), a 45° direction, and a Y-axis direction(90°) from a point at which a black pixel changes to a white pixel andwhich is nearest to a crossing point of grid lines in the X-direction.

In the present example, a character image is divided into a grid having8×8 grid units, and black pixels are searched for in eight directions,i.e. 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315° directions asillustrated in FIG. 5( b).

The method for extracting the feature of a grid direction differs interms of a direction of searching and in terms of a point from whichsearching is performed. Examples of the method are described in JapaneseUnexamined Patent Publication No. 2000-181994 (Tokukai 2000-181994) etc.

The character-image-feature extracting section 14 extracts the featuresof all the standard character images that are stored in the font-sampleDB 13. The character-image-feature extracting section 14 stores, in thefont-feature dictionary 15, the result of extraction of the standardcharacter images stored in the font-sample DB 13, and thus generates thefont-feature dictionary 15.

FIG. 6 illustrates a process in which the character-image-featureextracting section 14 generates the font-feature dictionary 15. In thecharacter-image-feature extracting section 14, a font standardizingsection 14 a extracts a standard character image from thecharacter-feature-sample DB 13 and a character-image-feature extractingsection 14 b extracts the feature of the standard character imageextracted by the font-standardizing section 14 a. Referring to thefont-sample DB 13, a feature-classifying section 14 c classifies thefeature of each standard character image, and stores the feature in thefont-feature dictionary 15.

As described above, the character-image-feature extracting section 14 bcalculates suitable values of the features of differently weightedstandard character images with respect to each character, and thusobtains standard features of the standard character images.

The character-image-feature extracting section 14 b can generatedifferent character-image-feature dictionaries by weighting differentcharacter styles and character sizes. By mixing image features of pluralcharacter-styles and generating a font-feature dictionary with respectto each character-image-feature, it is possible to make automaticindexing and managing of an image document including plural characterstyles and plural character sizes in a satisfactory manner.

The following explains the image-document DB 19, theimage-document-feature DB 20, the significant-region initial processingsection 18, and the character-image-feature extracting section 14 thatconstitute an image-document-feature extracting section 31 forperforming the image-document-feature extracting process.

When an image document is supplied from the image document input section21 to the image-document DB 19, the image-document DB 19 assigns, to theimage document, a document ID for identification and stores the imagedocument.

When a new image document is stored in the image-document DB 19, thesignificant-region initial processing section 18 specifies and clips asignificant region in the image document based on the image data, andtransmits a character image to the character-image-feature extractingsection 14.

FIG. 9 illustrates a state where three regions T1, T2, and T3 arespecified as significant regions in an image document 50. As illustratedin FIG. 9, title portions in the image document 50 are clipped as asignificant region T.

The character image clipped by the significant-region initial processingsection 18 and sent to the character-image-feature extracting section 14is generally an image of a character sequence including pluralcharacters. Accordingly, hereinafter, an explanation will be made as toa case where the character image sent from the significant-regioninitial processing section 18 is an image of a character sequence.

In the present embodiment, the significant-region initial processingsection 18 specifies and clips the significant region T through aprojective method and a statistical analysis of connected regions. Thesignificant region T mainly corresponds to a title portion. Thesignificant region T may be specified and clipped through variousconventional methods that are described in Japanese Unexamined PatentPublication No. 1997-319747 (Tokukaihei 9-319747) and JapaneseUnexamined Patent Publication No. 1996-153110 (Tokukaihei 8-153110).

As described above, only the significant region T such as a titleportion is specified and clipped, and whole character regions (textregions) in an image document are not specified and extracted. Thisallows reducing the amount of information to be searched and shorteningthe time for searching.

However, specifying only a significant region such as a title portionwithout specifying whole text regions is not necessarily required in thepresent embodiment. That is, whole text regions may be specified andextracted.

The character-image-feature extracting section 14 divides an image of acharacter sequence input from the significant-region initial processingsection 18 into images of individual characters, and then extracts thefeatures of the images of individual characters as with the case ofgenerating the font-feature dictionary 15. The extracted features arestored in the image-document-feature DB 20 with respect to each imagedocument.

Information indicative of the features of an image of a charactersequence included in the significant region T clipped by thesignificant-region initial processing section 18 is stored in theimage-document-feature DB 20 as features (feature vectors) of individualcharacters that constitute the character sequence.

As illustrated in FIG. 9, with respect to the image document 50, thefeatures of character images of individual character sequences includedin whole significant regions T1, T2, and T3 . . . having been extracted,i.e. the features of images of characters that constitute individualcharacter sequences are stored with the document ID of the imagedocument 50.

The following explains the character-image-feature extracting section14, the font-feature dictionary 15, the feature-matching section 16, theindex-information DR 17, and the image-document-feature DB 20 thatconstitute an index-information generating section 32 that performs theindex-information generating process.

Functions of the character-image-feature extracting section 14, thefont-feature dictionary 15, and the image-document-feature DB 20 havebeen already explained above.

The feature-matching section 16 reads out the features of the characterimages included in the significant region T of the image document fromthe image-document-feature DB 20, generates an index matrix (which willbe mentioned later) based on the read-out features and with reference tothe font-feature dictionary 15, and generates index information of theimage document.

One index information is generated with respect to one image document,and an index matrix included in the index information is generated withrespect to each significant region T. Therefore, when one image documentincludes plural significant regions T, index information of the imagedocument includes plural index matrices.

FIG. 7 illustrates a process for generating the index-information DB 17.As described above, when an image document is input and stored in theimage-document DB 19, the character-image-feature extracting section 14b extracts the features of character images of a character sequenceincluded in each significant region T, and stores the features in theimage-document-feature DB 20.

The feature-matching section 16 reads out the features of the images ofthe character sequence included in each significant region T from theimage-document-feature DB 20, matches each single character of thecharacter sequence with a standard character image included in thefont-feature dictionary 15, and generates an index matrix with respectto each significant region T.

The feature-matching section 16 adds, to each index matrix, otherinformation of the image document such as a document ID and informationindicative of a location where the image document is stored in theimage-document DB 19, and thus generates index information, and storesthe index information in the index-information DB 17.

FIG. 8 illustrates an example of a process in which the feature-matchingsection 16 generates an index matrix. FIG. 8 is an explanatory drawingfor illustrating generation of an index matrix for eight characterimages included in a character sequence

that is included in the significant region T3 in FIG. 9.

The character sequence

is divided into single character images

and

Such division may be performed through a conventional method.

The eight characters

are given numbers 1 to 8, respectively, so that

is given 1,

is given 2, . . . , and

is given 8. These numbers are regarded as row-numbers of the indexmatrix.

Each of the eight character images is subjected to a process as follows:the feature of the character image

is extracted from the image-document-feature DB 20 indicated by areference sign A in FIG. 8 (S1), and N candidate characters are selectedin the order of similarity of features (in the order of high degree ofmatching) with reference to the font-feature dictionary 15 (S2).

The N candidate characters having been extracted in the order of highdegree of matching are given numbers corresponding to the order ofextraction. The numbers are regarded as column-numbers of the indexmatrix. A character similarity value (similarity value) indicative ofthe degree of matching between a search character included in a searchkeyword and a candidate character is set according to the column-number.

A table indicated by a reference numeral 100 in FIG. 8 shows thecontents of the index matrix of the character sequence

For example, with respect to the character image

that is a fifth character, candidate characters

. . . , and

are selected at a row of row-number 5 so that the character with thehighest matching is positioned at a 1st column. In the table 100, forexample, the position of the candidate character

in the index matrix is [1, 1], the position of the candidate character

is [4, 2], and the position of the candidate character

is [5, N].

In the table 100 in FIG. 8, candidate characters corresponding toindividual characters of the character sequence are shown with a sign“◯”.

Row-number M of the index matrix is equal to the number of charactersincluded in an image of a character sequence that is clipped as thesignificant region T by the significant-region initial processingsection 18. Column-number N is equal to the number of candidatecharacters that are selected for one character. Accordingly, the presentinvention allows flexibly setting the number of elements in the indexmatrix, i.e. the number of candidate characters by changing the numberof dimensions (number of columns) of the matrix index. Consequently, thepresent invention allows exact searching of an image document withlittle omissions.

How to store information of selected candidate characters in the indexmatrix is suitably determined according to how to input a searchkeyword. For example, in the case of inputting a search keyword via akeyboard 1, candidate characters are stored as information such ascharacter codes, in order to perform searching based on the searchkeyword inputted via the keyboard.

In the case of inputting a search keyword as image data via an imagescanner 2 etc., candidate characters are stored as informationindicative of features (feature vectors), in order to perform searchingby extracting the features of the search keyword (feature vectors) andcomparing the feature vectors of the search keyword with the featurevectors of the candidate characters.

FIG. 9 illustrates an example of data disposition of index informationin the index-information DB 17. In index information of the imagedocument 50 in which significant regions T1, T2, T3, . . . , and Tnexist, index matrices for the significant regions T1, T2, T3, . . . ,and Tn are disposed in a linear manner. In the example of FIG. 9, adocument ID is disposed foremost, and thereafter the index matrices aredisposed, and information indicative of a storage location is disposedat the end. In FIG. 9, “5×N” is a size of an index matrix and indicates5 rows and N columns.

By disposing the index information as described above, it is possible topromptly specify the location where the image document is stored in theimage-document DB 19 and the location where the significant region Texists in the image document, thereby using the image document and thesignificant region T for displaying the result of searching.

Further, it is possible to add other properties to the index informationaccording to actual requests.

The following explains a search section 22 for performing a searchingprocess using index information. FIG. 10 is an explanatory drawing forillustrating the function of the search section 22 and the searchingprocess. The search section 22 includes an index-matrix searchingprocess section 22 a, a character-similarity-value storage section(storage section) 22 b, a similarity-degree calculating section 22 c, adisplay-order determining section (order determining section) 22 d, andan image-document extracting section 22 e.

A search keyword is input from the keyword input section 24 to theindex-matrix searching process section 22 a. Examples of the keywordinput section 24 include the keyboard 1 and the image scanner 2.

The index-matrix searching process section 22 a searches theindex-information DB 17 in order to detect an index matrix that includesthe input search keyword. The index-matrix searching process section 22a divides the search keyword into single search characters, and searchesfor an index matrix that includes a search character. When there existsan index matrix that includes a search character, the index-matrixsearching process section 22 a acquires information indicative of alocation where the search character is matched with a candidatecharacter in the index matrix. An example of a procedure for extractingthe index matrix will be explained later using a flowchart in FIG. 11.

The character-similarity-value storage section 22 b stores: informationindicative of a matching position acquired by the index-matrix searchingprocess section 22 a; and a character-similarity value according to acolumn-number of the matching position.

After the index-matrix searching process section 22 a detects all indexmatrices, the similarity-degree calculating section 22 c calculates thedegree of similarity between the detected index matrices and the searchkeyword.

Calculation of the degree of similarity is performed through apredetermined similarity-degree calculating method by using informationindicative of the matching position and the character-similarity valuethat is stored in the character-similarity-value storage section 22 b.Calculation of the degree of similarity will be explained later withreference to FIGS. 12 and 13.

In the present embodiment, the character-similarity-value storagesection 22 b stores information indicative of the matching position andthe character-similarity value according to the column-number of thematching position. However, the present invention may be arranged sothat the character-similarity-value storage section 22 b stores only theinformation indicative of the matching position, and thesimilarity-degree calculating section 22 c acquires thecharacter-similarity value from the information indicative of thematching position.

The display-order determining section 22 d determines the order ofdisplay on the basis of information indicative of similarity-degreecalculated by the similarity-degree calculating section 22 c. Thedisplay-order determining section 22 d determines the order of displayso that the search-result display section displays image documentssequentially from an image document having an index matrix with highsimilarity-degree.

The image-document extracting section 22 e reads out image data of imagedocuments from the image-document DB 19 so that image documents aredisplayed in the order determined by the display-order determiningsection 22 d. Then, the image-document extracting section 22 e outputsthe image data to the search-result display section 25, and thesearch-result display section 25 displays the image documents.

The search-result display section 25 displays the image documentsaccording to the order of display. The display may be thumbnail display.An example of the search-result display section 25 is the display device3.

The following explains a search procedure. FIG. 11 is a flowchartshowing a search procedure performed by the search section 22. When asearch keyword including R number of character sequences is input andinstruction of searching is given, the index-matrix searching processsection 22 a extracts a first search character of a search keyword(S11).

Next, the index-matrix searching process section 22 a searches all indexmatrices in the index-information DB 17 for the first search character(S12).

When all of the index matrices have been searched, the index-matrixsearching process section 22 a judges whether the first search characteris found or not. When the first search character is not found at all,the procedure goes to S19. When the first search character is found, theprocedure goes to S14.

In S14, the index-matrix searching process section 22 a stores, in thecharacter-similarity-value storage section 22 b, a matching position anda character-similarity value in an index matrix that includes the firstsearch character.

Subsequently, the index-matrix searching process section 22 a extractsall index matrices that include the first search character (S15). Then,the index-matrix searching process section 22 a extracts a second searchcharacter that is positioned next to the first search character in thesearch keyword, and in search of the second character, the index-matrixsearching process section 22 a searches the index matrices that havebeen extracted in S15 and that include the first search character (S16).

When all of the index matrices having been extracted in S15 have beensearched, the index-matrix searching process section 22 a judges whetherthe second search character is found or not (S17). When the secondsearch character is not found at all, the procedure goes to S19 as withthe aforementioned case. When the second search character is found, theprocedure goes to S18.

In S18, the index-matrix searching process section 22 a stores, in thecharacter-similarity-value storage section 22 b, a matching position anda character-similarity value in an index matrix that includes the secondsearch character.

Subsequently, the index-matrix searching process section 22 a goes backto S16, extracts a third search character that is positioned next to thesecond search character in the search keyword, and in search of thethird search character, the index-matrix searching process section 22 asearches the index matrices that have been extracted in S15 and thatinclude the first search character.

When all of the index matrices have been searched, the index-matrixsearching process section 22 a judges whether the third search characteris found or not (S17). When the third search character is not found atall, the procedure goes to S19. When the third search character isfound, the procedure goes to S18 again, and the index-matrix searchingprocess section 22 a searches for a search character that is positionednext to the third search character in the search keyword.

The index-matrix searching process section 22 a performs the steps S16to S18, i.e. a narrowing search to search the index matrices having beenextracted in S15 and including the first search character so as to findthe second and subsequent search characters, until the index-matrixsearch processing section 22 a judges in S17 that no character is foundor until the index-matrix search processing section 22 a judges that allsearch characters included in the search keyword have been searched for.Thereafter, the procedure goes to S19.

In S19, the index-matrix search processing section 22 a extracts thesecond search character that is positioned next to the first searchcharacter in the search keyword. Then, the index-matrix searchprocessing section 22 a judges whether other search character exists,that is, whether all search characters have been searched for (S20).When all search characters have not been searched for, the proceduregoes back to S12.

In the same way as the above procedures, the index-matrix searchingprocess section 22 a searches all index matrices in theindex-information DB 17 for the second search character. When the secondsearch character is found, the index-matrix searching process section 22a stores a matching position and a character-similarity value of theindex matrix and then goes to S15. Then, the index-matrix searchingprocess section 22 a repeatedly performs S16 to S18, i.e. a narrowingsearch to search all index matrices including the second searchcharacter so as to find the third and subsequent search characters thatare positioned next to the second search character and thereafter in thesearch keyword.

With respect to the third and subsequent search characters, theindex-matrix searching process section 22 a performs the aforementionedsearching, i.e. a procedure in which a next search character isextracted in S19, index matrices including the next search character areextracted, and the index matrices are narrowed down using subsequentsearch characters.

When the index-matrix searching process section 22 a extracts all searchcharacters in the search keyword in S19, and when the index-matrixsearching process section 22 a judges in S20 that all search keywordshave been searched for, the procedure goes to S21.

In S21, the similarity-degree calculating section 22 c calculates thedegree of similarity between the search keyword and the index matricesaccording to a reference for similarity degree, which will be mentionedlater.

Then, the display-order determining section 22 d determines the order ofdisplay so that an image document having an index matrix including highsimilarity-degree is firstly displayed. The image document extractingsection 22 e acquires image data of image documents from theimage-document DB 19, and the search-result display section 25 displaysthe image documents sequentially in the order of high similarity-degree(S22).

With reference to FIGS. 12 and 13, the following explains a method forcalculating the degree of similarity between an index matrix and asearch keyword according to a reference for similarity degree in thesimilarity-degree calculating section 22 c.

A block indicated by a reference numeral 101 in FIG. 12 describes searchconditions. A block indicated by a reference numeral 102 describes arelative relationship between a supposed search keyword for calculatingthe degree of similarity and an index matrix. Under the searchconditions shown in the block 101 and with a relative relationshipbetween the search keyword and the index matrix shown in the block 102,the degree of similarity between the search keyword and the index matrixis calculated by an expression shown in a block 103.

First, an explanation is made as to the search conditions in the block101. The number of characters included in the search keyword is R. Afirst search character is C1, a second search character is C2, . . . ,and an R_(th) search character is Cr.

The index matrix to be searched is an M×N matrix. That is, the number ofcharacters included in a character sequence image clipped as asignificant region T is M, and the number of candidate charactersselected as candidates for each character of the character sequence isN.

Character-similarity values each indicative of similarity between asearch character and a candidate character are set according topositions in the index matrix. Accordingly, the character-similarityvalues are disposed in a matrix that has the same dimensions as theindex matrix. That is, a character-similarity-value matrix Weight is anM×N matrix. For example, Weight [i][j] indicates a character-similarityvalue in a case where a candidate character whose position is [i, j](=Index [i][j]) in the index matrix is matched. In the presentembodiment, when two character-similarity values are positioned at thesame column-number [j], the two character-similarity values are the sameregardless of the row-number [i].

A similarity-degree-weighting factor Q for rows is a weight to bemultiplied with character-similarity values in two adjacent rows in theindex matrix when search characters are matched with candidatecharacters in the two adjacent rows. When search characters are matchedwith candidate characters in two adjacent rows, there is a highpossibility that the two rows contain two continuous characters,respectively, of the search keyword.

When the similarity-degree-weighting factor Q is set to be large,character-similarity values for two rows where matching continuouslyoccurs greatly contribute to similarity-degree calculated by thesimilarity-degree calculating section 22 c, whereascharacter-similarity-values for rows that are not adjacent to each otherreduce the similarity-degree calculated by the similarity-degreecalculating section 22 c. That is, when the similarity-degree-weightingfactor Q is set to be large, the result of searching gets closer to theresult of searching performed with respect to each vocabulary. Incontrast, when the similarity-degree-weighting factor Q is set to besmall, the result of searching gets closer to the result of searchingperformed with respect to each character.

A character-similarity value for matching of a search character C1 isrepresented by W1, a character-similarity value for matching of a searchcharacter C2 is represented by W2, . . . , and a character-similarityvalue for matching of a search character Cr is represented by Wr.

The following explains a relative relationship between a search keywordand an index matrix, which is assumed to calculate similarity-degree andis shown in the block 102.

The relationship between the search keyword and the index matrix is suchthat all of the search characters C1, C2, . . . , and Cr are matchedwith candidate characters in the index matrix. Positions of thecandidate characters with which the search characters C1, C2, . . . ,and Cr arc matched in the index matrix, that is, matching positions ofthe candidate characters are represented by [C1 i, C1 j], [C2 i, C2 j],. . . , and [Cri, Crj].

Another relative relationship is represented by an equation (1) in theblock 102

C(k+1)i=Cki+1, C(m+1)i=Cmi+1(m>k)

where k and m represent relative positions of search characters that areincluded in the search keyword, C(k+1)i represents a row-number of acandidate character with which a (k+1)th search character is matched inthe index matrix, and Cki represents a row-number of a candidatecharacter with which a kth search character is matched in the indexmatrix.

Accordingly, C(k+1)i=Cki+1 indicates that the row-number of thecandidate character with which the (k+1)th search character is matchedin the index matrix is obtained by adding 1 to the row-number of thecandidate character with which the kth search character is matched inthe index matrix. In other words, C(k+1)i=Cki+1 indicates that the(k+1)th search character and the kth search character of the searchkeyword are matched with candidate characters in two adjacent rows,respectively, of the index matrix.

Similarly, C(m+1)i=Cmi+1 (m>k) indicates that (m+1)th search characterand the mth search character of the search keyword are matched withcandidate characters in two adjacent rows, respectively, of the indexmatrix.

When such relative relationship exists between the search keyword andthe index matrix, the degree of similarity between the search keywordand the index matrix is represented by an equation (2) shown in theblock 103

SimDegree=W1+W2+ . . . +W(k−1)+Q*(Wk+W(k+1))+ . . .+W(m−1)+Q*(Wm+W(m+1))+ . . . +Wr

where W1 is a character-similarity value for matching of the firstsearch character C1, W2 is a character-similarity value for matching ofthe second search character C2, W(k−1) is a character-similarity valuefor matching of the (k−1)th search character C(k−1), W(k) is acharacter-similarity value for matching of the kth search character Ck,W(k+1) is a character-similarity value for matching of the (k+1)thsearch character C(k+1), W(m−1) is a character-similarity value formatching of the (m−1)th search character C(m−1), W(m) is acharacter-similarity value for matching of the mth search character Cm,W(m+1) is a character-similarity value for matching of the (m+1)thsearch character C(m+1), and Wr is a character-similarity value formatching of the r-th search character Cr.

As described above, the similarity-degree is calculated by adding up(accumulating) character-similarity values W for all the searchcharacters included in the search keyword.

Q*(Wk+W(k+1)) in the equation (2) indicates that thecharacter-similarity values Wk and W(k+1) are multiplied by thesimilarity-degree-weighting factor Q for rows because the kth searchcharacter Ck and the (k+1)th search character C(k+1) are matched withcandidate characters in two adjacent rows in the index matrix.Q*(Wm+W(m+1)) in the equation (2) indicates similarly.

W(k−1) and Wk are not multiplied by the similarity-degree-weightingfactor Q because (k−1)th search character and kth search characterincluded in the search keyword are not matched with candidate charactersin two adjacent rows. The similar can be said about W(m−1) and Wm.

In the relative relationship between the search keyword and the indexmatrix which is shown in the block 102 of FIG. 12, it is supposed thatall of the search characters C1, C2, . . . , and Cr are matched with thecandidate characters in the index matrix. Accordingly, in the equation(2), the character-similarity values W1 to Wr for all the searchcharacters are accumulated.

However, this is only an example. For example, when search charactershave the relative relationship represented by the equation (1) and thesearch characters C1 and Cr are not matched with any candidate characterin the index matrix, the similarity-degree is calculated by thefollowing equation. Because the number of character-similarity degreesto be accumulated is smaller, the similarity-degree is lower.

SimDegree=W2+ . . . +W(k−1)+Q*(Wk+W(k+1))+ . . . +W(m−1)+Q*(Wm+W(m+1))+. . . +W(r−1)

Further, when all of the search characters C1, C2, . . . , and Cr arematched with the candidate characters in the index matrix and when the(k+1)th search character and the kth search character of the searchkeyword are matched with candidate characters in two adjacent rows andthe (k+2)th search character and the (k+1)th search character of thesearch keyword are matched with candidate characters in two adjacentrows, the similarity-degree is represented by the following equation

SimDegree=W1+W2+ . . . +W(k−1)+Q*(Wk+W(k+1))+W(k+2))+ . . . +Wr

In this case, too, W(k−1) and Wk are not multiplied by thesimilarity-degree-weighting factor Q because (k−1)th search characterand kth search character included in the search keyword are not matchedwith candidate characters in two adjacent rows.

With reference to FIG. 13, the following explains a specific example ofcalculation of the similarity-degree. Here, the degree of similaritybetween the index matrix for the character sequence

(see Table 100) shown in FIG. 8 and a search keyword

is calculated.

Search conditions are shown in a block 104 in FIG. 13. Asimilarity-value matrix Weight is M×N dimensions, a character-similarityvalue is Weight[i]=[1, 1−1/N, 1−2/N, . . . , 1/N] (i=0, 1, . . . , M−1),and a similarity-degree-weighting factor is set to be Q.

The search keyword

is divided into a first search character

and a second search character

, and each search character is searched for from candidate characters inthe index matrix.

Reference of the table 100 in FIG. 8 shows that the search character

is matched with [2, 2] of a position [i, j] in the index matrix, andthat the search character

is matched with [3, 1] in the index matrix.

Consequently, as shown in a block 105, a character-similarity value forthe search character

is (1−1/N), and a character-similarity value for the search character

is 1.

A row-number of the search character

is [2] and the row-number of the search character

is [3]. As shown in the table 100 in FIG. 8, the two search charactersare matched with candidate characters in two adjacent rows in the indexmatrix.

Accordingly, as shown in a block 106, the character-similarity value(1−1/N) for the search character

and the character-similarity value 1 for the search character “

are multiplied by the similarity-degree-weighting factor Q for rows, andas a result degree of similarity between the search keyword

and the index matrix for the character sequence

is SimDegree=Q*((1−1/N)+1).

By flexibly adjusting parameters such as weighting (character-similarityvalue) in the character-similarity-value matrix and thesimilarity-degree-weighting factor Q for rows according to a user'srequest, the degree of similarity between the search keyword and theindex matrix allows providing a more ideal result of searching.

Using the keyboard 1 etc., a user can suitably set parameters such asweighting (character-similarity value) in the character-similarity-valuematrix and the similarity-degree weighting factor Q for rows accordingto necessity.

Such indexing and matching based on the features of an image ispreferably applicable to indexing and searching of an image documentwith multiple languages. Such indexing and matching does not performrecognition of characters, and therefore has small amount ofcalculation. The present invention is applicable to an image documentwith various languages as well as Chinese language.

Lastly, the following explains a search process with a semantic-analysisfunction. As shown in FIG. 1, the image document processing device ofthe present embodiment includes the semantic-analysis section 23 betweenthe search keyword input section 24 and the search section 22. FIG. 14illustrates the search process with the semantic-analysis function.

The semantic-analysis section 23 includes a semantic-analysis processsection 23 a and a notion dictionary 23 b. When a search keyword issupplied from the search keyword input section 24, the semantic-analysisprocess section 23 a refers to the notion dictionary 23 b and analyzes avocabulary of the search keyword.

For example, when a search keyword “

(Chinese-Japanese relationship) is input, the semantic-analysis processsection 23 a inputs, in the search section 22, three words

(China),

(Japan), and

(relationship) for example as words relevant to the search keyword

A relation of “or” exists among the three words

and

and a search expression is

or

or

The search expression

or

or

is input to the search section 22. The search section 22 searches theindex-information DB 17 and extracts an image document including

an image document including

and an image document including

This allows searching for not only an image document directly includingthe input search keyword but also for relevant image documents.

Each block of the image document processing device, in particular, thecharacter-style normalizing section 12, the character-image-featureextracting section 14, the feature matching section 16, thesignificant-region initial processing section 18, the search section 22,the semantic-analysis section 23 etc. may be realized by hardware logicor may be realized by software by using a CPU as described below.

Namely, the image document processing device 10 include: a CPU (centralprocessing unit) for executing a program for realizing functions of eachblock; a ROM (read only memory) that stores the program; a RAM (randomaccess memory) that develops the program; the storage device (storagemedium) such as a memory for storing the program and various data; andthe like. The object of the present invention can be realized in such amanner that the image document processing device is provided with acomputer-readable storage medium for storing program codes (such asexecutable program, intermediate code program, and source program) ofprograms of the image document processing device which programs serve assoftware for realizing the functions, and a computer (alternatively, CPUor MPU) reads out and executes the program codes stored in the storagemedium.

The storage medium is, for example, tapes such as a magnetic tape and acassette tape, or discs such as magnetic discs (e.g. a Floppy Disc® anda hard disc), and optical discs (e.g. CD-ROM, MO, MD, DVD, and CD-R).Further, the storage medium may be cards such as an IC card (including amemory card) and an optical card, or semiconductor memories such as maskROM, EPROM, EEPROM, and flash ROM.

Further, the image document processing device may be arranged so as tobe connectable to a communication network so that the program code issupplied to the image document processing device through thecommunication network. The communication network is not particularlylimited. Examples of the communication network include the Internet,intranet, extranet, LAN, ISDN, VAN, CATV communication network, virtualprivate network, telephone network, mobile communication network, andsatellite communication network. Further, a transmission medium thatconstitutes the communication network is not particularly limited.Examples of the transmission medium include (i) wired lines such as IEEE1394, USB, power-line carrier, cable TV lines, telephone lines, and ADSLlines and (ii) wireless connections such as IrDA and remote controlusing infrared ray, Bluetooth®, 802.11, HDR, mobile phone network,satellite connections, and terrestrial digital network. Note that thepresent invention can be also realized by the program codes in the formof a computer data signal embedded in a carrier wave, which is theprogram that is electrically transmitted.

As described above, the image document processing device of the presentinvention includes: a font-feature dictionary for storing features ofimages of individual characters; a character-sequence clipping sectionfor clipping an image of a character sequence including a plurality ofcharacters in an input image document; an image-feature extractingsection for dividing the image of the character sequence into images ofindividual characters and extracting features of the images ofindividual characters; a feature matching section for selecting N (N isan integer of more than 1) images of characters as candidate charactersin an order of high degree of matching of features so as to generate anM×N index matrix where M (M is an integer of more than 1) is the numberof characters in the character sequence, the candidate characters beingselected from the font-feature dictionary based on the features of theimages of individual characters that have been extracted by theimage-feature extracting section; an index-information storage sectionfor storing the index matrix so that the index matrix is related to theinput image document; and a search section for searching theindex-information storage section with respect to each search characterincluded in a search keyword in an input search expression, andextracting one or more image documents each including an index matrixthat includes the search character.

With the arrangement, the image-feature extracting section divides, intoimages of individual characters, the image of the character sequence inthe image document that is specified and clipped by thecharacter-sequence clipping section, and the image-feature extractingsection extracts features of the images of individual characters. Then,the feature matching section selects N (N is an integer of more than 1)images of characters as candidate characters in an order of high degreeof matching of features so as to generate an M×N index matrix for theextracted character sequence, the candidate characters being selected,based on the features of the images of individual characters, from thefont-feature dictionary for storing features of images of individualcharacters.

The index-information storage section stores the generated index matrixso that the index-matrix is related to the input image document. Insearching, the index matrix is used as index information for searchingan image document.

Consequently, it is possible to automatically specify a charactersequence in a character region of an image document without user'soperation and without OCR recognition, and to generate index informationfor the image document based on a feature of an image of the specifiedcharacter sequence.

Further, because features of images of individual characters areextracted and a plurality of candidate characters whose image featuresare similar are selected, suitably setting the number of the selectedcandidate characters allows exact searching without spending much timein recognition of characters unlike OCR recognition, and withoutomission of recognition.

Further, the font-feature dictionary is generated to store features ofimages of individual characters, and the feature matching sectiondivides the image of the extracted character sequence into images ofindividual characters to perform matching of features of the images.This allows the generated index matrix to have high accuracy.

In searching, the search section searches the index-information storagesection with respect to each search character included in a searchkeyword in an input search expression, and extracts one or more imagedocuments each including an index matrix that includes the searchcharacter.

In this manner, index matrices are analyzed with respect to each searchcharacter included in a search keyword so as to detect an index matrixincluding the search keyword. This assures total searching based onsearching for single characters.

Further, the image document processing device of the present inventionmay be arranged so that the character-sequence clipping section clips aheadline region in the input image document.

With the arrangement, the character-sequence clipping section clips aheadline region in the input image document. This allows generating anindex matrix for a headline in the image document.

This allows reducing the number of index matrices to be generated forone image document, compared with the case of generating index matricesfor whole text regions in the image document. This allows searching foran image document according to a headline, which allows efficientkeyword searching.

Further, the image document processing device of the present inventionmay be arranged so that the character-sequence clipping section clips asignificant region in the input image document through a projectivemethod and a statistical analysis of connected regions.

This allows the character-sequence clipping section to easily clip aheadline region in an image document.

Further, the image document processing device of the present inventionmay be arranged so that the image-feature extracting section extractsthe features of the images of individual characters based on acombination of (i) a feature of a grid direction and (ii) a feature of aperiphery of an image of each character.

This allows efficiently extracting the features of the images ofindividual characters according to a difference in character shapes.

Further, the image document processing device of the present inventionmay be arranged so that the feature matching section selects thecandidate characters by matching the extracted features of the images ofindividual characters with features of images of all characters storedin the font-feature dictionary.

This allows generating an index matrix by selecting the candidatecharacters in the most exact order.

Further, the image document processing device of the present inventionmay be arranged so that N which is the number of candidate charactersselected by the feature matching section is variable.

By allowing the N to be variable, it is possible to generate an indexmatrix according to user's requests. For example, reduction of N allowsthe index matrix to have less number of dimensions, which shortens atime for searching and increases search accuracy and search exactness.In contrast, increase in N allows the index matrix to have more numberof dimensions, which eliminates omission in searching and increasessearch ratio.

Further, the image document processing device of the present inventionmay be arranged so that the search section extracts the image documentsin an order of firstly extracting an image document having an indexmatrix in which a search character included in the search keyword ismatched with a candidate character at a high position.

In the technique disclosed in Japanese Unexamined Patent Publication No.1998-74250 (Tokukaihei 10-74250), a search keyword is converted into asequence of representative character codes, which does not allowspecifying the position of a character of the keyword in asimilar-character category. Consequently, characters belonging to thesame similar-character category show the same degree of relevanceregardless of whether the characters are more similar or less similar.As a result, it is impossible to present image documents exactly in theorder sequentially from the most relevant image document.

However, with the arrangement of the present invention, the imagedocuments are extracted in an order of firstly extracting an imagedocument having an index matrix in which a search character included inthe search keyword is matched with a candidate character at a highposition. This allows showing the result of searching so that imagedocuments are shown in an order of high relevance.

Further, the image document processing device of the present inventionmay be arranged so that each element of the index matrix is given asimilarity value corresponding to a position of the element, and thesearch section includes: an index-matrix searching process section forsearching index matrices with respect to each search character includedin the search keyword so as to detect an index matrix including thesearch character, and for storing, in a storage section, informationindicative of a matching position of the search character in the indexmatrix, the information being stored in combination with informationindicative of an image document including the index matrix; asimilarity-degree calculating section for calculating a degree ofsimilarity between the search keyword and the index matrix byaccumulating a similarity value of each search character based on theinformation indicative of a matching position; and an order-determiningsection for determining an order of extracting image documents based ona result of calculation performed by the similarity-degree calculatingsection.

With the arrangement, each element of the index matrix is given asimilarity value corresponding to a position of the element. Thesimilarity value is a value indicative of similarity between a searchcharacter and a candidate character in the index matrix. The similarityvalue is set according to the order of candidate characters selected bythe feature matching section. In general, the element having a smallercolumn-number is given a larger similarity value.

The index-matrix searching process section of the search sectionsearches index matrices with respect to each search character includedin the search keyword so as to detect an index matrix including thesearch character, and stores, in a storage section, informationindicative of a matching position of the search character in the indexmatrix, the information being stored in combination with informationindicative of an image document including the index matrix.

The similarity-degree calculating section of the search sectioncalculates the degree of similarity between the search keyword and theindex matrix by accumulating a similarity value of each search characterbased on the information indicative of a matching position; and theorder-determining section determines the order of displaying imagedocuments so that image documents are extracted in the order of highrelevance.

This allows extracting image documents in the order of firstlyextracting an image document including an index matrix in which a searchcharacter included in the search keyword is matched with a candidatecharacter at a high position, and thus allows showing the result ofsearching so that image documents are shown in the order of relevance.

Further, the image document processing device of the present inventionmay be arranged so that when the similarity-degree calculating sectionjudges, based on the information indicative of a matching position, thatsearch characters are matched with candidate characters in adjacent rowsrespectively in the index matrix, the similarity-degree calculatingsection causes similarity values in the adjacent rows to be multipliedby a similarity-degree-weighting factor for rows and then accumulatesthe similarity values thus multiplied.

With the arrangement, when the similarity-degree calculating sectionjudges that search characters are matched with candidate characters inadjacent rows respectively in the index matrix, similarity values forcorresponding rows are multiplied by a similarity-degree-weightingfactor for rows. That is, when searching is performed with respect toeach character and when continuous search characters in the searchkeyword are included in an index matrix, the result of searching isshown such that an image document including the index matrix is rankedhigher. This allows effective searching based on simple calculation.

Further, the image document processing device of the present inventionmay be arranged so that the similarity value given to each element ofthe index matrix is variable.

By allowing the similarity value to be variable, the image documentprocessing device of the present invention can realize searchingaccording to user's request. For example, in a case of not changing Nthat is the number of candidate characters, it is possible to change theresult of searching by setting similarity values corresponding tocolumn-numbers of N candidate characters so that similarity valuescorresponding to 1st to A-th column-numbers are large and similarityvalues corresponding to subsequent column-numbers are small. This allowsthe image document processing device of the present invention toflexibly respond to user's request.

Further, the image document processing device of the present inventionmay be arranged so that the similarity-degree-weighting factor for rowsis variable.

By allowing the similarity-degree-weighting factor for rows to bevariable, it is possible to realize searching according to user'srequest. For example, when the similarity-degree-weighting factor is setto be large, character-similarity values for two rows where matchingcontinuously occurs greatly contribute to a calculatedsimilarity-degree. Consequently, the result of searching gets closer tothe result of searching performed with respect to each vocabulary. Incontrast, when the similarity-degree-weighting factor is set to besmall, the result of searching gets closer to the result of searchingperformed with respect to each character. This allows the image documentprocessing device to perform searching that flexibly responds to theuser's request.

Further, the image document processing device of the present inventionmay be arranged so that a font-feature dictionary generating section forgenerating the font-feature dictionary includes the image-featureextracting section.

With the arrangement, the font-feature dictionary generating sectionshares the image-feature extracting section in common with theindex-information generating section. Consequently, it is possible togenerate a font-feature dictionary by extracting features of fonts withrespect to each of different character styles and character sizes. Inaddition, it is possible to easily generate a font-feature dictionarywith respect to each of different character styles and character sizesof characters of different language.

Further, the image document processing method of the present inventionincludes the steps of: (I) clipping an image of a character sequenceincluding a plurality of characters in an input image document; (ii)dividing the image of the character sequence into images of individualcharacters and extracting features of the images of individualcharacters; (III) selecting N (N is an integer of more than 1) images ofcharacters as candidate characters in an order of high degree ofmatching of features so as to generate an M×N index matrix where M (M isan integer of more than 1) is the number of characters in the charactersequence, the candidate characters being selected, based on the featuresof the images of individual characters that have been extracted in thestep (ii), from a font-feature dictionary for storing features of imagesof individual characters; (IV) storing the index matrix so that theindex matrix is related to the input image document; and (V) searchingthe index-information with respect to each search character included ina search keyword in an input search expression, and extracting one ormore image documents each including an index matrix that includes thesearch character.

Further, the present invention includes: a program for causing acomputer to function as each section of the image document processingdevice of the present invention; and a computer-readable storage mediumin which the program is stored.

The image document processing device may be realized by hardware or maybe realized by a computer executing a program. Specifically, the programof the present invention is a program for causing a computer to functionas each section as described above, and the program is stored in thestorage medium of the present invention.

When a computer executes the program, the computer functions as theimage document processing device. Accordingly, the computer attains thesame result as the image document processing device.

The invention being thus described, it will be obvious that the same waymay be varied in many ways. Such variations are not to be regarded as adeparture from the spirit and scope of the invention, and all suchmodifications as would be obvious to one skilled in the art are intendedto be included within the scope of the following claims.

1. An image document processing device, comprising: a font-featuredictionary for storing features of images of individual characters; acharacter-sequence clipping section for clipping an image of a charactersequence including a plurality of characters in an input image document;an image-feature extracting section for dividing the image of thecharacter sequence into images of individual characters and extractingfeatures of the images of individual characters; a feature matchingsection for selecting N (N is an integer of more than 1) images ofcharacters as candidate characters from the font-feature dictionary soas to generate an M×N index matrix where M (M is an integer of morethan 1) is the number of characters in the character sequence, thecandidate characters being selected in an order of high degree offeature-matching with the features of the images of individualcharacters that have been extracted by the image-feature extractingsection; an index-information storage section for storing the indexmatrix so that the index matrix is related to the input image document;and a search section for searching the index-information storage sectionwith respect to each search character included in a search keyword in aninput search expression, and extracting one or more image documents eachincluding an index matrix that includes the search character.
 2. Theimage document processing device as set forth in claim 1, wherein thecharacter-sequence clipping section clips a headline region in the inputimage document.
 3. The image document processing device as set forth inclaim 1, wherein the character-sequence clipping section clips asignificant region in the input image document through a projectivemethod and a statistical analysis of connected regions.
 4. The imagedocument processing device as set forth in claim 1, wherein theimage-feature extracting section extracts the features of the images ofindividual characters based on a combination of (i) a feature of a griddirection and (ii) a feature of a periphery of an image of eachcharacter.
 5. The image document processing device as set forth in claim1, wherein the feature matching section selects the candidate charactersby matching the extracted features of the images of individualcharacters with features of images of all characters stored in thefont-feature dictionary.
 6. The image document processing device as setforth in claim 1, wherein N is variable.
 7. The image documentprocessing device as set forth in claim 1, wherein the search sectionextracts the image documents in an order of including an index matrixwith high degree of feature-matching, the image documents beingextracted based on information indicative of a matching position of eachsearch character included in the search keyword in the index matrix. 8.The image document processing device as set forth in claim 1, whereineach element of the index matrix is given a similarity valuecorresponding to a position of the element, and the search sectionincludes: an index-matrix searching process section for searching indexmatrices with respect to each search character included in the searchkeyword so as to detect an index matrix including the search character,and for storing, in a storage section, information indicative of amatching position of the search character in the index matrix, theinformation being stored in combination with information indicative ofan image document including the index matrix; a similarity-degreecalculating section for calculating a degree of similarity between thesearch keyword and the index matrix by accumulating a similarity valueof each search character based on the information indicative of amatching position; and an order-determining section for determining anorder of extracting image documents based on a result of calculationperformed by the similarity-degree calculating section.
 9. The imagedocument processing device as set forth in claim 8, wherein when thesimilarity-degree calculating section judges, based on the informationindicative of a matching position, that search characters are matchedwith candidate characters in adjacent rows respectively in the indexmatrix, the similarity-degree calculating section causes similarityvalues in the adjacent rows to be multiplied by asimilarity-degree-weighting factor for rows and then accumulates thesimilarity values thus multiplied.
 10. The image document processingdevice as set forth in claim 8, wherein the similarity value given toeach element of the index matrix is variable.
 11. The image documentprocessing device as set forth in claim 9, wherein thesimilarity-degree-weighting factor for rows is variable.
 12. The imagedocument processing device as set forth in claim 1, wherein afont-feature dictionary generating section for generating thefont-feature dictionary includes the image-feature extracting section.13. An image document processing method, comprising the steps of: (I)clipping an image of a character sequence including a plurality ofcharacters in an input image document; (II) dividing the image of thecharacter sequence into images of individual characters and extractingfeatures of the images of individual characters; (III) selecting N (N isan integer of more than 1) images of characters as candidate charactersfrom a font-feature dictionary for storing features of images ofindividual characters, so as to generate an M×N index matrix where M (Mis an integer of more than 1) is the number of characters in thecharacter sequence, the candidate characters being selected in an orderof high degree of feature-matching with the features of the images ofindividual characters that have been extracted in the step (II); (IV)storing the index matrix so that the index matrix is related to theinput image document; and (V) searching the index-information withrespect to each search character included in a search keyword in aninput search expression, and extracting one or more image documents eachincluding an index matrix that includes the search character.
 14. Aprogram for causing a computer to function as each section of an imagedocument processing device as set forth in claim
 1. 15. Acomputer-readable storage medium in which a program as set forth inclaim 14 is stored.